The second group of methods leverages deep neural networks in order to compute a discriminative global descriptor in a learned manner.
One of the first is PointNetVLAD [1]}, which uses PointNet architecture to extract local features, followed by NetVLAD pooling producing a global descriptor.
It was followed by a number of later works [2]}, [3]}, [4]}, [5]} using the same principle: local features extracted from the 3D point cloud are pooled to yield a discriminative global descriptor.
However all these works operate on relatively small point clouds constructed by accumulating and downsampling a few consecutive scans from a 2D LiDAR.
Thus, they do not scale well to larger point clouds generated by a single 360\(^\circ \)  sweep from a 3D LiDAR with an order of magnitude more points.
To mitigate this limitation another line of methods uses an intermediary representation of an input point cloud before feeding it to a neural network. DiSCO [6]} first converts a point cloud into a multi-layered representation, then uses a convolutional neural network to extract features in a polar domain and produce a global descriptor.
